Terminal X matches senior investor judgment 88.6% with GPT-6.1

An investment firm’s edge is built deal by deal, but the judgment behind each decision usually lives in the heads of a few senior investors. When they leave, the firm takes on what Terminal X ⁠ (opens in a new window) calls memory debt: the firm may keep the documents, but it loses the reasoning.

Terminal X’s Capital Memory operating system is designed to pay that debt down. The company’s forward-deployed engineers, former finance and investment professionals at JPMorgan and Blackstone, sit with each customer’s investment teams, design the firm’s agentic architecture, and encode how the firm invests into agents the firm owns, deployed on-premise or in its own cloud.

OpenAI models, including GPT‑6.1 Sol and GPT‑6 Luna, handle the reasoning inside those agents. And Terminal X’s Eval Agent grades the agents’ output against the firm’s standards. GPT‑6.1 Sol helped the Eval Agent catch about 25% more recommendations that strayed from a fund’s mandate and ran the same workflows at approximately 30% lower cost per query than the next-best model.

The agents work inside each firm’s private data room, a dedicated environment for enterprises to share and manage sensitive investment data. The agents process documents in English, Korean, Japanese, and Chinese, and store what they learn as a firm Memory: a versioned record of how one desk makes one kind of decision, including its past analyses, committee decisions, workflow instructions, and house views.

Engineers turn each Memory into a repeatable workflow on Terminal X’s platform, where every production query is auditable with its full trail: the steps taken, sources retrieved, model used, and the reasoning.

To check agents’ answers against the firm’s own standard, Terminal X built an Eval Agent. It’s an orchestrated workflow on GPT‑6.1 Sol and other OpenAI models designed to read every agent output the way a VP reads an analyst’s work.

For each customer, Terminal X builds agents designed to invest the way the firm’s best investors do. Terminal X engineers spend weeks onsite with the customer, reverse-engineering the firm’s trusted memos and capturing processes that were never written down, such as how the credit desk screens a borrower or which sources the co-investment team pulls before a memo.

Next, those processes are encoded in hundreds of agents, customized by asset class and strategy, with sub-agents for each investment stage. GPT‑6.1 Sol handles the stages that require judgment and a huge context window: synthesizing the investment thesis, identifying key considerations, and assessing fit with the firm’s mandate. GPT‑6 Luna runs the high-volume steps where token efficiency matters more than depth.

According to Terminal X, customers see significant savings as a result of this approach, including one private equity firm that reported $2.4 million in annualized savings from automated due diligence and investment memo workflows.